ArticleSensors (Basel, Switzerland)2019
Reconstruction of 3D Object Shape Using Hybrid Modular Neural Network Architecture Trained on 3D Models from
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- HUMANNET-A Two-Tiered Deep Neural Network Architecture for Self-Occluding Humanoid Pose Reconstruction.Sensors (Basel, Switzerland) · 2021Article
- Auto-Refining Reconstruction Algorithm for Recreation of Limited Angle Humanoid Depth Data.Sensors (Basel, Switzerland) · 2021Article
- A novel no-sensors 3D model reconstruction from monocular video frames for a dynamic environment.PeerJ. Computer science · 2021Article
- 3D Object Reconstruction from Imperfect Depth Data Using Extended YOLOv3 Network.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
4 authors.
Funding
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Abstract
Depth-based reconstruction of three-dimensional (3D) shape of objects is one of core problems in computer vision with a lot of commercial applications. However, the 3D scanning for point cloud-based video streaming is expensive and is generally unattainable to an average user due to required setup of multiple depth sensors. We propose a novel hybrid modular artificial neural network (ANN) architecture, which can reconstruct smooth polygonal meshes from a single depth frame, using a priori knowledge. The architecture of neural network consists of separate nodes for recognition of object type and reconstruction thus allowing for easy retraining and extension for new object types. We performed recognition of nine real-world objects using the neural network trained on the
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